#!/usr/bin/env python3
"""Compute APM row-level metrics for model output files."""

from __future__ import annotations

import argparse
import re
from pathlib import Path
from typing import Iterable, Iterator, Optional, Tuple

from apm_metrics import compute_metrics, load_json_or_jsonl, write_json


NOISE_RE = re.compile(r"N\d+")
JSON_EXTENSIONS = {".json", ".jsonl"}


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--input-root",
        type=Path,
        required=True,
        help="Root containing <model>/<noise>/results.jsonl outputs.",
    )
    parser.add_argument(
        "--output-root",
        type=Path,
        default=Path("metrics"),
        help="Directory where metric JSON files will be written.",
    )
    parser.add_argument(
        "--model",
        help="Model name to use when --input-root directly contains N*/ folders or is a file.",
    )
    parser.add_argument(
        "--noise",
        help="Noise label to use when --input-root is a single file.",
    )
    return parser.parse_args()


def is_json_data_file(path: Path) -> bool:
    return path.suffix in JSON_EXTENSIONS and path.name not in {"metrics.json", "compiled.json"}


def preferred_files(noise_dir: Path) -> Iterable[Path]:
    for preferred in ("results.jsonl", "results.json"):
        path = noise_dir / preferred
        if path.exists():
            return [path]
    return sorted(path for path in noise_dir.iterdir() if path.is_file() and is_json_data_file(path))


def direct_noise_dirs(root: Path) -> bool:
    dirs = [path for path in root.iterdir() if path.is_dir()]
    return bool(dirs) and all(NOISE_RE.fullmatch(path.name) for path in dirs)


def discover_inputs(
    input_root: Path,
    model_override: Optional[str],
    noise_override: Optional[str],
) -> Iterator[Tuple[str, str, Path, Path]]:
    """Yield model, noise, input path, and output-relative metric path."""

    if input_root.is_file():
        if not model_override or not noise_override:
            raise SystemExit("Single-file mode requires --model and --noise")
        yield model_override, noise_override, input_root, Path(model_override) / noise_override / "metrics.json"
        return

    if direct_noise_dirs(input_root):
        model = model_override or input_root.name
        for noise_dir in sorted(path for path in input_root.iterdir() if path.is_dir()):
            for file_path in preferred_files(noise_dir):
                out_name = "metrics.json" if file_path.stem == "results" else f"{file_path.stem}_metrics.json"
                yield model, noise_dir.name, file_path, Path(model) / noise_dir.name / out_name
        return

    for model_dir in sorted(path for path in input_root.iterdir() if path.is_dir()):
        model = model_override or model_dir.name
        for noise_dir in sorted(path for path in model_dir.iterdir() if path.is_dir()):
            if noise_override and noise_dir.name != noise_override:
                continue
            for file_path in preferred_files(noise_dir):
                out_name = "metrics.json" if file_path.stem == "results" else f"{file_path.stem}_metrics.json"
                yield model, noise_dir.name, file_path, Path(model) / noise_dir.name / out_name


def evaluate_file(input_path: Path, output_path: Path, model: str, noise: str) -> Tuple[int, int]:
    records = load_json_or_jsonl(input_path)
    metrics = []
    skipped = 0

    for record in records:
        row = compute_metrics(record, model=model, noise=noise)
        if row is None:
            skipped += 1
            continue
        metrics.append(row)

    write_json(metrics, output_path)
    return len(metrics), skipped


def main() -> None:
    args = parse_args()

    total_rows = 0
    total_skipped = 0
    total_files = 0

    for model, noise, input_path, rel_output_path in discover_inputs(
        args.input_root,
        args.model,
        args.noise,
    ):
        output_path = args.output_root / rel_output_path
        rows, skipped = evaluate_file(input_path, output_path, model=model, noise=noise)
        total_rows += rows
        total_skipped += skipped
        total_files += 1
        print(f"{model}/{noise}: {rows} rows, {skipped} skipped -> {output_path}")

    print(
        f"Processed {total_files} files with {total_rows} metric rows "
        f"and {total_skipped} skipped rows"
    )


if __name__ == "__main__":
    main()